Dynamic vehicle pose estimation and tracking based on motion feedback for LiDARs

doi:10.1007/s10489-022-03576-3

	Dynamic vehicle pose estimation and tracking based on motion feedback for LiDARs
	Xu, Fengyu 1,3; Wang, Zhiling 2,3,4; Wang, Hanqi 1,3; Lin, Linglong 2,3,4; Liang, Huawei 2,3,4
刊名	APPLIED INTELLIGENCE
	2022-05-07
关键词	Vehicle tracking Pose estimation Motion feedback Matched filtering
ISSN号	0924-669X
DOI	10.1007/s10489-022-03576-3
通讯作者	Lin, Linglong(linll@iim.ac.cn) ; Liang, Huawei(hwliang@iim.ac.cn)
英文摘要	This paper presents a novel dynamic vehicle tracking framework, achieving accurate pose estimation and tracking in urban environments. For vehicle tracking with laser scanners, pose estimation extracts geometric information of the target from a point cloud clustering unit, which plays an essential role in tracking tasks. However, the point cloud acquired from laser scanners only provides distance measurements to the object surface facing the sensor, leading to nonnegligible pose estimation errors. To address this issue, we take the motion information of targets as feedback to assist vehicle detection and pose estimation. In addition, the heading normalization vehicle model and a robust target size estimation method are introduced to deduce the pose of a vehicle with 2D matched filtering. Furthermore, considering the mobility of vehicles, we utilize the interactive multitude model (IMM) to capture multiple motion patterns. Compared to existing methods in the literature, our method can be applied to spatially sparse or incomplete point cloud observations. Experimental results demonstrate that our vehicle tracking framework achieves promising performance, and its real-time capability is also validated in real traffic scenarios.
资助项目	National Key Research and Development Program of China[2020AAA0108103] ; Key Science and Technology Project of Anhui[202103a05020007] ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province
WOS关键词	3D ; SEGMENTATION ; NETWORK
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000791874500001
资助机构	National Key Research and Development Program of China ; Key Science and Technology Project of Anhui ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/130791]
专题	中国科学院合肥物质科学研究院
通讯作者	Lin, Linglong; Liang, Huawei
作者单位	1.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Applicat & Innovat Res Inst Robot & Intelligent M, Hefei 230088, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230088, Peoples R China 4.Anhui Engn Lab Intelligent Driving Technol, Hefei 230088, Peoples R China
推荐引用方式 GB/T 7714	Xu, Fengyu,Wang, Zhiling,Wang, Hanqi,et al. Dynamic vehicle pose estimation and tracking based on motion feedback for LiDARs[J]. APPLIED INTELLIGENCE,2022.
APA	Xu, Fengyu,Wang, Zhiling,Wang, Hanqi,Lin, Linglong,&Liang, Huawei.(2022).Dynamic vehicle pose estimation and tracking based on motion feedback for LiDARs.APPLIED INTELLIGENCE.
MLA	Xu, Fengyu,et al."Dynamic vehicle pose estimation and tracking based on motion feedback for LiDARs".APPLIED INTELLIGENCE (2022).